FYS-2021 Machine Learning - 10 ECTS
Knowledge - The student is able to:
- Describe fundamental concepts behind machine learning in modern society.
- Describe a number of machine learning application areas in society.
- Discuss and select appropriate data sources applicable to a given machine learning approach.
- Discuss and select appropriate approaches, such as unsupervised versus supervised, when it comes to the choice of machine learning algorithm to use.
Skills - The student is able to:
- explain the application domains of machine learning methodology and machine learning algorithms for data analysis in society and research.
- analyse data for knowledge extraction and inference by applying various machine learning methods and algorithms.
General expertise - The student is able to:
- understand the role of machine learning in modern society in the context of data analysis
- implement and apply fundamental machine learning methods and algorithms for analysis of data in e.g. Matlab or Python
Portfolio assessment of 3 project assignments and a final 4 hours written examination. All modules in the portfolio are assessed as a whole and one combined grade is given.
Assessment scale: Letter grades A-F, where the letters A-E are passed and F is failed.
Re-sit examination (section 22): There is no access to a re-sit examination in this course.
Postponed examination (sections 17 and 21): Students with valid grounds for absence will be offered a postponed examination. Postponed project assignments are arranged during the semester if possible, otherwise early in the following semester. Postponed written examination is held early in the following semester. See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.
See indicated sections in Regulations for examinations at the UiT The arctic university of Norway for more information.
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-2021
- Responsible unit
- Institutt for fysikk og teknologi